Overview

Dataset statistics

Number of variables45
Number of observations899
Missing cells11432
Missing cells (%)28.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory368.1 KiB
Average record size in memory419.3 B

Variable types

Numeric20
Categorical25

Alerts

age is highly overall correlated with years and 2 other fieldsHigh correlation
painloc is highly overall correlated with cp and 1 other fieldsHigh correlation
painexer is highly overall correlated with relrest and 3 other fieldsHigh correlation
relrest is highly overall correlated with painexer and 2 other fieldsHigh correlation
cp is highly overall correlated with painloc and 4 other fieldsHigh correlation
trestbps is highly overall correlated with trestbpdHigh correlation
chol is highly overall correlated with met and 3 other fieldsHigh correlation
smoke is highly overall correlated with cigs and 1 other fieldsHigh correlation
cigs is highly overall correlated with smoke and 4 other fieldsHigh correlation
years is highly overall correlated with age and 2 other fieldsHigh correlation
fbs is highly overall correlated with exerefHigh correlation
famhist is highly overall correlated with exeref and 1 other fieldsHigh correlation
prop is highly overall correlated with restefHigh correlation
nitr is highly overall correlated with pro and 1 other fieldsHigh correlation
pro is highly overall correlated with nitr and 1 other fieldsHigh correlation
proto is highly overall correlated with restecg and 5 other fieldsHigh correlation
thaldur is highly overall correlated with proto and 4 other fieldsHigh correlation
thaltime is highly overall correlated with proto and 4 other fieldsHigh correlation
met is highly overall correlated with chol and 2 other fieldsHigh correlation
thalach is highly overall correlated with thaldur and 4 other fieldsHigh correlation
thalrest is highly overall correlated with thalach and 1 other fieldsHigh correlation
tpeakbps is highly overall correlated with xhypoHigh correlation
tpeakbpd is highly overall correlated with restefHigh correlation
trestbpd is highly overall correlated with trestbps and 2 other fieldsHigh correlation
exang is highly overall correlated with painexer and 5 other fieldsHigh correlation
oldpeak is highly overall correlated with thaltime and 2 other fieldsHigh correlation
slope is highly overall correlated with exang and 1 other fieldsHigh correlation
rldv5 is highly overall correlated with rldv5e and 1 other fieldsHigh correlation
rldv5e is highly overall correlated with restecg and 2 other fieldsHigh correlation
ca is highly overall correlated with painloc and 2 other fieldsHigh correlation
restef is highly overall correlated with painexer and 11 other fieldsHigh correlation
restwm is highly overall correlated with dig and 3 other fieldsHigh correlation
exeref is highly overall correlated with diuretic and 20 other fieldsHigh correlation
exerwm is highly overall correlated with age and 7 other fieldsHigh correlation
num is highly overall correlated with oldpeak and 2 other fieldsHigh correlation
cathef is highly overall correlated with chol and 4 other fieldsHigh correlation
diuretic is highly overall correlated with exeref and 1 other fieldsHigh correlation
dm is highly overall correlated with ageHigh correlation
htn is highly overall correlated with exerefHigh correlation
dig is highly overall correlated with restef and 1 other fieldsHigh correlation
dataset is highly overall correlated with chol and 7 other fieldsHigh correlation
restecg is highly overall correlated with proto and 1 other fieldsHigh correlation
sex is highly overall correlated with exeref and 1 other fieldsHigh correlation
xhypo is highly overall correlated with tpeakbpsHigh correlation
thal is highly overall correlated with restefHigh correlation
painloc has 282 (31.4%) missing valuesMissing
painexer has 282 (31.4%) missing valuesMissing
relrest has 286 (31.8%) missing valuesMissing
trestbps has 59 (6.6%) missing valuesMissing
htn has 34 (3.8%) missing valuesMissing
chol has 30 (3.3%) missing valuesMissing
smoke has 669 (74.4%) missing valuesMissing
cigs has 420 (46.7%) missing valuesMissing
years has 432 (48.1%) missing valuesMissing
fbs has 90 (10.0%) missing valuesMissing
dm has 804 (89.4%) missing valuesMissing
famhist has 422 (46.9%) missing valuesMissing
dig has 68 (7.6%) missing valuesMissing
prop has 66 (7.3%) missing valuesMissing
nitr has 65 (7.2%) missing valuesMissing
pro has 63 (7.0%) missing valuesMissing
diuretic has 82 (9.1%) missing valuesMissing
proto has 112 (12.5%) missing valuesMissing
thaldur has 56 (6.2%) missing valuesMissing
thaltime has 453 (50.4%) missing valuesMissing
met has 105 (11.7%) missing valuesMissing
thalach has 55 (6.1%) missing valuesMissing
thalrest has 56 (6.2%) missing valuesMissing
tpeakbps has 63 (7.0%) missing valuesMissing
tpeakbpd has 63 (7.0%) missing valuesMissing
trestbpd has 59 (6.6%) missing valuesMissing
exang has 55 (6.1%) missing valuesMissing
xhypo has 58 (6.5%) missing valuesMissing
oldpeak has 62 (6.9%) missing valuesMissing
slope has 308 (34.3%) missing valuesMissing
rldv5 has 425 (47.3%) missing valuesMissing
rldv5e has 142 (15.8%) missing valuesMissing
ca has 608 (67.6%) missing valuesMissing
restef has 871 (96.9%) missing valuesMissing
restwm has 869 (96.7%) missing valuesMissing
exeref has 897 (99.8%) missing valuesMissing
exerwm has 894 (99.4%) missing valuesMissing
thal has 477 (53.1%) missing valuesMissing
cathef has 588 (65.4%) missing valuesMissing
exeref is uniformly distributedUniform
chol has 172 (19.1%) zerosZeros
cigs has 153 (17.0%) zerosZeros
years has 153 (17.0%) zerosZeros
thaltime has 70 (7.8%) zerosZeros
oldpeak has 362 (40.3%) zerosZeros

Reproduction

Analysis started2022-12-04 22:32:19.318045
Analysis finished2022-12-04 22:33:22.273169
Duration1 minute and 2.96 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct50
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.480534
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:22.360169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4358937
Coefficient of variation (CV)0.17643604
Kurtosis-0.38068102
Mean53.480534
Median Absolute Deviation (MAD)7
Skewness-0.18318938
Sum48079
Variance89.036091
MonotonicityNot monotonic
2022-12-04T23:33:22.498168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 51
 
5.7%
58 40
 
4.4%
55 39
 
4.3%
52 36
 
4.0%
56 36
 
4.0%
57 35
 
3.9%
62 35
 
3.9%
51 35
 
3.9%
59 34
 
3.8%
53 33
 
3.7%
Other values (40) 525
58.4%
ValueCountFrequency (%)
28 1
 
0.1%
29 3
 
0.3%
30 1
 
0.1%
31 2
 
0.2%
32 5
0.6%
33 2
 
0.2%
34 7
0.8%
35 10
1.1%
36 6
0.7%
37 11
1.2%
ValueCountFrequency (%)
77 2
 
0.2%
76 2
 
0.2%
75 3
 
0.3%
74 7
0.8%
73 1
 
0.1%
72 4
 
0.4%
71 5
 
0.6%
70 7
0.8%
69 13
1.4%
68 9
1.0%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
1
711 
0
188 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 711
79.1%
0 188
 
20.9%

Length

2022-12-04T23:33:22.610168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:22.706168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 711
79.1%
0 188
 
20.9%

Most occurring characters

ValueCountFrequency (%)
1 711
79.1%
0 188
 
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 711
79.1%
0 188
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
Common 899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 711
79.1%
0 188
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 711
79.1%
0 188
 
20.9%

painloc
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing282
Missing (%)31.4%
Memory size47.3 KiB
1.0
568 
0.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1851
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 568
63.2%
0.0 49
 
5.5%
(Missing) 282
31.4%

Length

2022-12-04T23:33:22.796171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:22.893169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 568
92.1%
0.0 49
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0 666
36.0%
. 617
33.3%
1 568
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1234
66.7%
Other Punctuation 617
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 666
54.0%
1 568
46.0%
Other Punctuation
ValueCountFrequency (%)
. 617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 666
36.0%
. 617
33.3%
1 568
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 666
36.0%
. 617
33.3%
1 568
30.7%

painexer
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing282
Missing (%)31.4%
Memory size47.3 KiB
1.0
366 
0.0
251 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1851
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 366
40.7%
0.0 251
27.9%
(Missing) 282
31.4%

Length

2022-12-04T23:33:22.979168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:23.078168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 366
59.3%
0.0 251
40.7%

Most occurring characters

ValueCountFrequency (%)
0 868
46.9%
. 617
33.3%
1 366
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1234
66.7%
Other Punctuation 617
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 868
70.3%
1 366
29.7%
Other Punctuation
ValueCountFrequency (%)
. 617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 868
46.9%
. 617
33.3%
1 366
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 868
46.9%
. 617
33.3%
1 366
19.8%

relrest
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing286
Missing (%)31.8%
Memory size47.2 KiB
1.0
412 
0.0
201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1839
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 412
45.8%
0.0 201
22.4%
(Missing) 286
31.8%

Length

2022-12-04T23:33:23.163168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:23.259168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 412
67.2%
0.0 201
32.8%

Most occurring characters

ValueCountFrequency (%)
0 814
44.3%
. 613
33.3%
1 412
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1226
66.7%
Other Punctuation 613
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 814
66.4%
1 412
33.6%
Other Punctuation
ValueCountFrequency (%)
. 613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1839
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 814
44.3%
. 613
33.3%
1 412
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 814
44.3%
. 613
33.3%
1 412
22.4%

cp
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
4
485 
3
202 
2
167 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 485
53.9%
3 202
22.5%
2 167
 
18.6%
1 45
 
5.0%

Length

2022-12-04T23:33:23.341167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:23.445168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 485
53.9%
3 202
22.5%
2 167
 
18.6%
1 45
 
5.0%

Most occurring characters

ValueCountFrequency (%)
4 485
53.9%
3 202
22.5%
2 167
 
18.6%
1 45
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 485
53.9%
3 202
22.5%
2 167
 
18.6%
1 45
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 485
53.9%
3 202
22.5%
2 167
 
18.6%
1 45
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 485
53.9%
3 202
22.5%
2 167
 
18.6%
1 45
 
5.0%

trestbps
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)7.1%
Missing59
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean132.10119
Minimum0
Maximum200
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:23.562170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.151127
Coefficient of variation (CV)0.14497316
Kurtosis2.9686121
Mean132.10119
Median Absolute Deviation (MAD)10
Skewness0.207565
Sum110965
Variance366.76567
MonotonicityNot monotonic
2022-12-04T23:33:23.696168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 128
14.2%
130 112
12.5%
140 100
 
11.1%
110 58
 
6.5%
150 56
 
6.2%
160 50
 
5.6%
125 28
 
3.1%
115 19
 
2.1%
135 18
 
2.0%
128 16
 
1.8%
Other values (50) 255
28.4%
(Missing) 59
 
6.6%
ValueCountFrequency (%)
0 1
 
0.1%
80 1
 
0.1%
92 1
 
0.1%
94 2
 
0.2%
95 6
 
0.7%
96 1
 
0.1%
98 1
 
0.1%
100 15
1.7%
101 1
 
0.1%
102 3
 
0.3%
ValueCountFrequency (%)
200 4
 
0.4%
192 1
 
0.1%
190 2
 
0.2%
185 1
 
0.1%
180 12
1.3%
178 3
 
0.3%
174 1
 
0.1%
172 2
 
0.2%
170 13
1.4%
165 2
 
0.2%

htn
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing34
Missing (%)3.8%
Memory size52.1 KiB
0.0
453 
1.0
412 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2595
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 453
50.4%
1.0 412
45.8%
(Missing) 34
 
3.8%

Length

2022-12-04T23:33:23.819169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:23.917168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 453
52.4%
1.0 412
47.6%

Most occurring characters

ValueCountFrequency (%)
0 1318
50.8%
. 865
33.3%
1 412
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1730
66.7%
Other Punctuation 865
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1318
76.2%
1 412
 
23.8%
Other Punctuation
ValueCountFrequency (%)
. 865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2595
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1318
50.8%
. 865
33.3%
1 412
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1318
50.8%
. 865
33.3%
1 412
 
15.9%

chol
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct213
Distinct (%)24.5%
Missing30
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean198.75949
Minimum0
Maximum603
Zeros172
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:24.021168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1175
median224
Q3269
95-th percentile334.2
Maximum603
Range603
Interquartile range (IQR)94

Descriptive statistics

Standard deviation111.83441
Coefficient of variation (CV)0.562662
Kurtosis0.0068477005
Mean198.75949
Median Absolute Deviation (MAD)46
Skewness-0.60498843
Sum172722
Variance12506.936
MonotonicityNot monotonic
2022-12-04T23:33:24.150167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
19.1%
254 10
 
1.1%
204 9
 
1.0%
219 9
 
1.0%
230 9
 
1.0%
216 9
 
1.0%
223 9
 
1.0%
220 9
 
1.0%
211 9
 
1.0%
260 8
 
0.9%
Other values (203) 616
68.5%
(Missing) 30
 
3.3%
ValueCountFrequency (%)
0 172
19.1%
85 1
 
0.1%
100 2
 
0.2%
117 1
 
0.1%
126 1
 
0.1%
129 1
 
0.1%
132 1
 
0.1%
139 1
 
0.1%
141 1
 
0.1%
142 1
 
0.1%
ValueCountFrequency (%)
603 1
0.1%
564 1
0.1%
529 1
0.1%
518 1
0.1%
491 1
0.1%
468 1
0.1%
466 1
0.1%
458 1
0.1%
417 1
0.1%
412 1
0.1%

smoke
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.9%
Missing669
Missing (%)74.4%
Memory size39.7 KiB
1.0
119 
0.0
111 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters690
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 119
 
13.2%
0.0 111
 
12.3%
(Missing) 669
74.4%

Length

2022-12-04T23:33:24.266168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:24.357167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 119
51.7%
0.0 111
48.3%

Most occurring characters

ValueCountFrequency (%)
0 341
49.4%
. 230
33.3%
1 119
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 460
66.7%
Other Punctuation 230
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 341
74.1%
1 119
 
25.9%
Other Punctuation
ValueCountFrequency (%)
. 230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 690
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 341
49.4%
. 230
33.3%
1 119
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 341
49.4%
. 230
33.3%
1 119
 
17.2%

cigs
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct25
Distinct (%)5.2%
Missing420
Missing (%)46.7%
Infinite0
Infinite (%)0.0%
Mean19.118998
Minimum0
Maximum99
Zeros153
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:24.442168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q330
95-th percentile51
Maximum99
Range99
Interquartile range (IQR)30

Descriptive statistics

Standard deviation18.296273
Coefficient of variation (CV)0.95696818
Kurtosis0.68357637
Mean19.118998
Median Absolute Deviation (MAD)20
Skewness0.89298688
Sum9158
Variance334.75359
MonotonicityNot monotonic
2022-12-04T23:33:24.554171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 153
 
17.0%
20 136
 
15.1%
40 63
 
7.0%
30 35
 
3.9%
10 21
 
2.3%
60 17
 
1.9%
50 12
 
1.3%
15 9
 
1.0%
2 6
 
0.7%
25 6
 
0.7%
Other values (15) 21
 
2.3%
(Missing) 420
46.7%
ValueCountFrequency (%)
0 153
17.0%
1 1
 
0.1%
2 6
 
0.7%
3 2
 
0.2%
4 2
 
0.2%
5 2
 
0.2%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
10 21
 
2.3%
ValueCountFrequency (%)
99 1
 
0.1%
80 3
 
0.3%
75 1
 
0.1%
70 1
 
0.1%
65 1
 
0.1%
60 17
 
1.9%
50 12
 
1.3%
45 1
 
0.1%
40 63
7.0%
35 2
 
0.2%

years
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct42
Distinct (%)9.0%
Missing432
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean18.796574
Minimum0
Maximum60
Zeros153
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:24.670169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q330
95-th percentile45
Maximum60
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.359145
Coefficient of variation (CV)0.87032589
Kurtosis-1.2588961
Mean18.796574
Median Absolute Deviation (MAD)19
Skewness0.19453293
Sum8778
Variance267.62162
MonotonicityNot monotonic
2022-12-04T23:33:24.798168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 153
 
17.0%
30 58
 
6.5%
20 53
 
5.9%
40 51
 
5.7%
25 26
 
2.9%
35 17
 
1.9%
15 15
 
1.7%
50 13
 
1.4%
10 9
 
1.0%
5 6
 
0.7%
Other values (32) 66
 
7.3%
(Missing) 432
48.1%
ValueCountFrequency (%)
0 153
17.0%
1 3
 
0.3%
2 1
 
0.1%
3 2
 
0.2%
4 3
 
0.3%
5 6
 
0.7%
6 3
 
0.3%
7 3
 
0.3%
8 2
 
0.2%
10 9
 
1.0%
ValueCountFrequency (%)
60 1
 
0.1%
54 2
 
0.2%
50 13
 
1.4%
48 1
 
0.1%
47 3
 
0.3%
45 6
 
0.7%
42 1
 
0.1%
41 2
 
0.2%
40 51
5.7%
38 2
 
0.2%

fbs
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing90
Missing (%)10.0%
Memory size51.0 KiB
0.0
674 
1.0
135 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 674
75.0%
1.0 135
 
15.0%
(Missing) 90
 
10.0%

Length

2022-12-04T23:33:24.916169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:25.018167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 674
83.3%
1.0 135
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0 1483
61.1%
. 809
33.3%
1 135
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1618
66.7%
Other Punctuation 809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1483
91.7%
1 135
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1483
61.1%
. 809
33.3%
1 135
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1483
61.1%
. 809
33.3%
1 135
 
5.6%

dm
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.1%
Missing804
Missing (%)89.4%
Memory size37.1 KiB
1.0
91 
0.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters285
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 91
 
10.1%
0.0 4
 
0.4%
(Missing) 804
89.4%

Length

2022-12-04T23:33:25.101166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:25.189168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 91
95.8%
0.0 4
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 99
34.7%
. 95
33.3%
1 91
31.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190
66.7%
Other Punctuation 95
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99
52.1%
1 91
47.9%
Other Punctuation
ValueCountFrequency (%)
. 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99
34.7%
. 95
33.3%
1 91
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99
34.7%
. 95
33.3%
1 91
31.9%

famhist
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.4%
Missing422
Missing (%)46.9%
Memory size44.6 KiB
1.0
269 
0.0
208 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1431
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 269
29.9%
0.0 208
23.1%
(Missing) 422
46.9%

Length

2022-12-04T23:33:25.265168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:25.357168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 269
56.4%
0.0 208
43.6%

Most occurring characters

ValueCountFrequency (%)
0 685
47.9%
. 477
33.3%
1 269
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 954
66.7%
Other Punctuation 477
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 685
71.8%
1 269
 
28.2%
Other Punctuation
ValueCountFrequency (%)
. 477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 685
47.9%
. 477
33.3%
1 269
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 685
47.9%
. 477
33.3%
1 269
 
18.8%

restecg
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size52.8 KiB
0.0
538 
2.0
182 
1.0
177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2691
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 538
59.8%
2.0 182
 
20.2%
1.0 177
 
19.7%
(Missing) 2
 
0.2%

Length

2022-12-04T23:33:25.435169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:25.538168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 538
60.0%
2.0 182
 
20.3%
1.0 177
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 1435
53.3%
. 897
33.3%
2 182
 
6.8%
1 177
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1794
66.7%
Other Punctuation 897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1435
80.0%
2 182
 
10.1%
1 177
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1435
53.3%
. 897
33.3%
2 182
 
6.8%
1 177
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1435
53.3%
. 897
33.3%
2 182
 
6.8%
1 177
 
6.6%

dig
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing68
Missing (%)7.6%
Memory size51.5 KiB
0.0
802 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2493
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 802
89.2%
1.0 29
 
3.2%
(Missing) 68
 
7.6%

Length

2022-12-04T23:33:25.636169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:25.737169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 802
96.5%
1.0 29
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 1633
65.5%
. 831
33.3%
1 29
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1662
66.7%
Other Punctuation 831
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1633
98.3%
1 29
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 831
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2493
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1633
65.5%
. 831
33.3%
1 29
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1633
65.5%
. 831
33.3%
1 29
 
1.2%

prop
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.4%
Missing66
Missing (%)7.3%
Memory size51.5 KiB
0.0
618 
1.0
214 
22.0
 
1

Length

Max length4
Median length3
Mean length3.0012005
Min length3

Characters and Unicode

Total characters2500
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 618
68.7%
1.0 214
 
23.8%
22.0 1
 
0.1%
(Missing) 66
 
7.3%

Length

2022-12-04T23:33:25.833169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:25.943171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 618
74.2%
1.0 214
 
25.7%
22.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1451
58.0%
. 833
33.3%
1 214
 
8.6%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1667
66.7%
Other Punctuation 833
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1451
87.0%
1 214
 
12.8%
2 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1451
58.0%
. 833
33.3%
1 214
 
8.6%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1451
58.0%
. 833
33.3%
1 214
 
8.6%
2 2
 
0.1%

nitr
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing65
Missing (%)7.2%
Memory size51.5 KiB
0.0
612 
1.0
222 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2502
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 612
68.1%
1.0 222
 
24.7%
(Missing) 65
 
7.2%

Length

2022-12-04T23:33:26.035168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:26.139169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 612
73.4%
1.0 222
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 1446
57.8%
. 834
33.3%
1 222
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1668
66.7%
Other Punctuation 834
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1446
86.7%
1 222
 
13.3%
Other Punctuation
ValueCountFrequency (%)
. 834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2502
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1446
57.8%
. 834
33.3%
1 222
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1446
57.8%
. 834
33.3%
1 222
 
8.9%

pro
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing63
Missing (%)7.0%
Memory size51.6 KiB
0.0
692 
1.0
144 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2508
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 692
77.0%
1.0 144
 
16.0%
(Missing) 63
 
7.0%

Length

2022-12-04T23:33:26.236170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:26.334167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 692
82.8%
1.0 144
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 1528
60.9%
. 836
33.3%
1 144
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1672
66.7%
Other Punctuation 836
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1528
91.4%
1 144
 
8.6%
Other Punctuation
ValueCountFrequency (%)
. 836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1528
60.9%
. 836
33.3%
1 144
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1528
60.9%
. 836
33.3%
1 144
 
5.7%

diuretic
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing82
Missing (%)9.1%
Memory size51.2 KiB
0.0
725 
1.0
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2451
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 725
80.6%
1.0 92
 
10.2%
(Missing) 82
 
9.1%

Length

2022-12-04T23:33:26.419168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:26.518168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 725
88.7%
1.0 92
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0 1542
62.9%
. 817
33.3%
1 92
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1634
66.7%
Other Punctuation 817
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1542
94.4%
1 92
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 817
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2451
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1542
62.9%
. 817
33.3%
1 92
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1542
62.9%
. 817
33.3%
1 92
 
3.8%

proto
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)1.8%
Missing112
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean37.081321
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:26.600167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q375
95-th percentile128.5
Maximum200
Range199
Interquartile range (IQR)74

Descriptive statistics

Standard deviation50.144559
Coefficient of variation (CV)1.3522862
Kurtosis0.016816182
Mean37.081321
Median Absolute Deviation (MAD)4
Skewness1.1521804
Sum29183
Variance2514.4768
MonotonicityNot monotonic
2022-12-04T23:33:26.689167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 324
36.0%
5 98
 
10.9%
75 73
 
8.1%
12 73
 
8.1%
100 70
 
7.8%
125 52
 
5.8%
50 34
 
3.8%
150 25
 
2.8%
25 16
 
1.8%
175 12
 
1.3%
Other values (4) 10
 
1.1%
(Missing) 112
 
12.5%
ValueCountFrequency (%)
1 324
36.0%
4 2
 
0.2%
5 98
 
10.9%
6 5
 
0.6%
12 73
 
8.1%
25 16
 
1.8%
50 34
 
3.8%
75 73
 
8.1%
100 70
 
7.8%
125 52
 
5.8%
ValueCountFrequency (%)
200 2
 
0.2%
175 12
 
1.3%
150 25
 
2.8%
130 1
 
0.1%
125 52
5.8%
100 70
7.8%
75 73
8.1%
50 34
3.8%
25 16
 
1.8%
12 73
8.1%

thaldur
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct86
Distinct (%)10.2%
Missing56
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean8.6558719
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:26.803167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q16
median8.1
Q310.5
95-th percentile16
Maximum24
Range23
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.7466174
Coefficient of variation (CV)0.43284113
Kurtosis0.87538479
Mean8.6558719
Median Absolute Deviation (MAD)2.1
Skewness0.80451432
Sum7296.9
Variance14.037142
MonotonicityNot monotonic
2022-12-04T23:33:26.929170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 93
 
10.3%
7 65
 
7.2%
6 60
 
6.7%
10 51
 
5.7%
8 49
 
5.5%
11 45
 
5.0%
12 45
 
5.0%
4 39
 
4.3%
5 35
 
3.9%
13 32
 
3.6%
Other values (76) 329
36.6%
(Missing) 56
 
6.2%
ValueCountFrequency (%)
1 1
 
0.1%
1.5 4
 
0.4%
1.7 1
 
0.1%
1.8 1
 
0.1%
2 11
1.2%
2.3 1
 
0.1%
2.5 1
 
0.1%
3 22
2.4%
3.1 2
 
0.2%
3.2 1
 
0.1%
ValueCountFrequency (%)
24 1
 
0.1%
21 1
 
0.1%
20 6
 
0.7%
19 12
1.3%
18 15
1.7%
17 5
 
0.6%
16.5 1
 
0.1%
16 6
 
0.7%
15 11
1.2%
14.4 1
 
0.1%

thaltime
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct64
Distinct (%)14.3%
Missing453
Missing (%)50.4%
Infinite0
Infinite (%)0.0%
Mean5.6903587
Minimum0
Maximum20
Zeros70
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:27.056169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile12
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.994673
Coefficient of variation (CV)0.70200724
Kurtosis0.17218972
Mean5.6903587
Median Absolute Deviation (MAD)3
Skewness0.50722906
Sum2537.9
Variance15.957412
MonotonicityNot monotonic
2022-12-04T23:33:27.186168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
7.8%
6 65
 
7.2%
3 57
 
6.3%
9 28
 
3.1%
8 20
 
2.2%
10 19
 
2.1%
4 17
 
1.9%
12 17
 
1.9%
5 16
 
1.8%
7.5 11
 
1.2%
Other values (54) 126
 
14.0%
(Missing) 453
50.4%
ValueCountFrequency (%)
0 70
7.8%
0.5 1
 
0.1%
0.7 1
 
0.1%
1 5
 
0.6%
1.5 3
 
0.3%
2 7
 
0.8%
2.5 2
 
0.2%
3 57
6.3%
3.5 5
 
0.6%
3.6 2
 
0.2%
ValueCountFrequency (%)
20 1
 
0.1%
19 2
0.2%
17.8 1
 
0.1%
17.5 1
 
0.1%
15.8 1
 
0.1%
15 3
0.3%
14.5 1
 
0.1%
14.3 1
 
0.1%
14 2
0.2%
13.5 1
 
0.1%

met
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)4.3%
Missing105
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean16.483123
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:27.313167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q310
95-th percentile100
Maximum200
Range198
Interquartile range (IQR)5

Descriptive statistics

Standard deviation30.772801
Coefficient of variation (CV)1.8669278
Kurtosis10.729135
Mean16.483123
Median Absolute Deviation (MAD)2
Skewness3.3785106
Sum13087.6
Variance946.96529
MonotonicityNot monotonic
2022-12-04T23:33:27.421167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7 107
11.9%
5 106
11.8%
6 102
11.3%
9 71
7.9%
10 58
 
6.5%
4 54
 
6.0%
8 52
 
5.8%
3 31
 
3.4%
100 31
 
3.4%
13 27
 
3.0%
Other values (24) 155
17.2%
(Missing) 105
11.7%
ValueCountFrequency (%)
2 22
 
2.4%
2.5 1
 
0.1%
3 31
 
3.4%
3.5 1
 
0.1%
4 54
6.0%
4.5 1
 
0.1%
5 106
11.8%
5.4 1
 
0.1%
5.8 1
 
0.1%
6 102
11.3%
ValueCountFrequency (%)
200 1
 
0.1%
150 21
2.3%
125 2
 
0.2%
100 31
3.4%
75 7
 
0.8%
50 11
 
1.2%
18 2
 
0.2%
17 2
 
0.2%
16 7
 
0.8%
15 3
 
0.3%

thalach
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct119
Distinct (%)14.1%
Missing55
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean137.29858
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:27.544168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile95
Q1120
median140
Q3157
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.965959
Coefficient of variation (CV)0.18912038
Kurtosis-0.48473155
Mean137.29858
Median Absolute Deviation (MAD)20
Skewness-0.19993508
Sum115880
Variance674.23103
MonotonicityNot monotonic
2022-12-04T23:33:27.666168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 42
 
4.7%
140 40
 
4.4%
120 35
 
3.9%
130 29
 
3.2%
160 26
 
2.9%
110 21
 
2.3%
125 20
 
2.2%
170 20
 
2.2%
122 16
 
1.8%
100 14
 
1.6%
Other values (109) 581
64.6%
(Missing) 55
 
6.1%
ValueCountFrequency (%)
60 1
0.1%
63 1
0.1%
67 1
0.1%
69 1
0.1%
70 1
0.1%
71 1
0.1%
72 2
0.2%
73 1
0.1%
77 1
0.1%
78 1
0.1%
ValueCountFrequency (%)
202 1
 
0.1%
195 1
 
0.1%
194 1
 
0.1%
192 1
 
0.1%
190 2
0.2%
188 2
0.2%
187 1
 
0.1%
186 2
0.2%
185 4
0.4%
184 4
0.4%

thalrest
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct75
Distinct (%)8.9%
Missing56
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean75.487544
Minimum37
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:27.799167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile55
Q165
median74
Q384
95-th percentile100
Maximum139
Range102
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.727961
Coefficient of variation (CV)0.19510452
Kurtosis0.73907914
Mean75.487544
Median Absolute Deviation (MAD)10
Skewness0.63667753
Sum63636
Variance216.91285
MonotonicityNot monotonic
2022-12-04T23:33:27.927168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 41
 
4.6%
74 33
 
3.7%
80 30
 
3.3%
68 30
 
3.3%
75 27
 
3.0%
72 26
 
2.9%
64 26
 
2.9%
73 25
 
2.8%
84 25
 
2.8%
78 24
 
2.7%
Other values (65) 556
61.8%
(Missing) 56
 
6.2%
ValueCountFrequency (%)
37 1
 
0.1%
39 1
 
0.1%
40 1
 
0.1%
43 1
 
0.1%
44 1
 
0.1%
46 2
0.2%
47 1
 
0.1%
49 4
0.4%
50 4
0.4%
51 1
 
0.1%
ValueCountFrequency (%)
139 1
 
0.1%
134 1
 
0.1%
125 3
0.3%
124 1
 
0.1%
120 3
0.3%
119 1
 
0.1%
116 1
 
0.1%
115 2
 
0.2%
112 2
 
0.2%
110 6
0.7%

tpeakbps
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct74
Distinct (%)8.9%
Missing63
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean171.64115
Minimum84
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:28.053167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile130
Q1155
median170
Q3190
95-th percentile220
Maximum240
Range156
Interquartile range (IQR)35

Descriptive statistics

Standard deviation25.734488
Coefficient of variation (CV)0.14993193
Kurtosis0.16268785
Mean171.64115
Median Absolute Deviation (MAD)18
Skewness0.040054662
Sum143492
Variance662.26389
MonotonicityNot monotonic
2022-12-04T23:33:28.174168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 97
 
10.8%
160 95
 
10.6%
170 81
 
9.0%
190 66
 
7.3%
200 58
 
6.5%
150 47
 
5.2%
140 40
 
4.4%
220 24
 
2.7%
210 21
 
2.3%
130 18
 
2.0%
Other values (64) 289
32.1%
(Missing) 63
 
7.0%
ValueCountFrequency (%)
84 1
 
0.1%
90 1
 
0.1%
92 1
 
0.1%
98 2
 
0.2%
100 1
 
0.1%
110 4
0.4%
112 1
 
0.1%
115 1
 
0.1%
116 1
 
0.1%
120 9
1.0%
ValueCountFrequency (%)
240 5
 
0.6%
235 1
 
0.1%
232 1
 
0.1%
230 14
1.6%
228 1
 
0.1%
224 1
 
0.1%
220 24
2.7%
216 1
 
0.1%
215 5
 
0.6%
210 21
2.3%

tpeakbpd
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct51
Distinct (%)6.1%
Missing63
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean87.293062
Minimum11
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:28.304169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile65
Q180
median88
Q3100
95-th percentile110
Maximum134
Range123
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.734586
Coefficient of variation (CV)0.16879447
Kurtosis0.92402607
Mean87.293062
Median Absolute Deviation (MAD)10
Skewness-0.13065041
Sum72977
Variance217.10802
MonotonicityNot monotonic
2022-12-04T23:33:28.432167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 162
18.0%
90 131
14.6%
100 114
12.7%
70 59
 
6.6%
110 43
 
4.8%
95 28
 
3.1%
85 25
 
2.8%
60 23
 
2.6%
75 23
 
2.6%
78 22
 
2.4%
Other values (41) 206
22.9%
(Missing) 63
 
7.0%
ValueCountFrequency (%)
11 1
 
0.1%
26 1
 
0.1%
40 2
 
0.2%
45 1
 
0.1%
50 2
 
0.2%
55 1
 
0.1%
56 2
 
0.2%
58 3
 
0.3%
60 23
2.6%
62 3
 
0.3%
ValueCountFrequency (%)
134 1
 
0.1%
130 2
 
0.2%
120 15
 
1.7%
118 4
 
0.4%
116 2
 
0.2%
115 8
 
0.9%
114 2
 
0.2%
112 1
 
0.1%
110 43
4.8%
108 2
 
0.2%

trestbpd
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)4.0%
Missing59
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean83.52381
Minimum0
Maximum120
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:28.552168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q180
median80
Q390
95-th percentile100
Maximum120
Range120
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.252563
Coefficient of variation (CV)0.12275018
Kurtosis4.9853062
Mean83.52381
Median Absolute Deviation (MAD)8
Skewness-0.54072702
Sum70160
Variance105.11505
MonotonicityNot monotonic
2022-12-04T23:33:28.664168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
80 258
28.7%
90 158
17.6%
70 88
 
9.8%
100 64
 
7.1%
85 42
 
4.7%
78 24
 
2.7%
95 23
 
2.6%
75 21
 
2.3%
82 15
 
1.7%
88 14
 
1.6%
Other values (24) 133
14.8%
(Missing) 59
 
6.6%
ValueCountFrequency (%)
0 1
 
0.1%
50 2
 
0.2%
58 1
 
0.1%
60 12
 
1.3%
64 4
 
0.4%
65 6
 
0.7%
66 1
 
0.1%
68 4
 
0.4%
70 88
9.8%
72 8
 
0.9%
ValueCountFrequency (%)
120 1
 
0.1%
110 7
 
0.8%
106 2
 
0.2%
105 5
 
0.6%
104 1
 
0.1%
102 1
 
0.1%
100 64
7.1%
98 12
 
1.3%
96 7
 
0.8%
95 23
 
2.6%

exang
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing55
Missing (%)6.1%
Memory size51.7 KiB
0.0
514 
1.0
330 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2532
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 514
57.2%
1.0 330
36.7%
(Missing) 55
 
6.1%

Length

2022-12-04T23:33:28.768167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:28.868170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 514
60.9%
1.0 330
39.1%

Most occurring characters

ValueCountFrequency (%)
0 1358
53.6%
. 844
33.3%
1 330
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1688
66.7%
Other Punctuation 844
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1358
80.5%
1 330
 
19.5%
Other Punctuation
ValueCountFrequency (%)
. 844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1358
53.6%
. 844
33.3%
1 330
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1358
53.6%
. 844
33.3%
1 330
 
13.0%

xhypo
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing58
Missing (%)6.5%
Memory size51.7 KiB
0.0
819 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2523
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 819
91.1%
1.0 22
 
2.4%
(Missing) 58
 
6.5%

Length

2022-12-04T23:33:28.953169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:29.051168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 819
97.4%
1.0 22
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 1660
65.8%
. 841
33.3%
1 22
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1682
66.7%
Other Punctuation 841
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1660
98.7%
1 22
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2523
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1660
65.8%
. 841
33.3%
1 22
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1660
65.8%
. 841
33.3%
1 22
 
0.9%

oldpeak
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct52
Distinct (%)6.2%
Missing62
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean0.87048984
Minimum-2.6
Maximum6.2
Zeros362
Zeros (%)40.3%
Negative12
Negative (%)1.3%
Memory size7.1 KiB
2022-12-04T23:33:29.150167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.5
Q31.5
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0805485
Coefficient of variation (CV)1.2413109
Kurtosis1.1476351
Mean0.87048984
Median Absolute Deviation (MAD)0.5
Skewness1.0277331
Sum728.6
Variance1.167585
MonotonicityNot monotonic
2022-12-04T23:33:29.271169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 362
40.3%
1 82
 
9.1%
2 75
 
8.3%
1.5 48
 
5.3%
3 28
 
3.1%
0.5 19
 
2.1%
2.5 16
 
1.8%
1.4 15
 
1.7%
0.6 14
 
1.6%
0.8 14
 
1.6%
Other values (42) 164
18.2%
(Missing) 62
 
6.9%
ValueCountFrequency (%)
-2.6 1
0.1%
-2 1
0.1%
-1.5 1
0.1%
-1.1 1
0.1%
-1 2
0.2%
-0.9 1
0.1%
-0.8 1
0.1%
-0.7 1
0.1%
-0.5 2
0.2%
-0.1 1
0.1%
ValueCountFrequency (%)
6.2 1
 
0.1%
5.6 1
 
0.1%
5 1
 
0.1%
4.2 2
 
0.2%
4 7
0.8%
3.8 1
 
0.1%
3.7 1
 
0.1%
3.6 4
0.4%
3.5 2
 
0.2%
3.4 2
 
0.2%

slope
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.7%
Missing308
Missing (%)34.3%
Memory size46.8 KiB
2.0
334 
1.0
196 
3.0
60 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1773
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 334
37.2%
1.0 196
21.8%
3.0 60
 
6.7%
0.0 1
 
0.1%
(Missing) 308
34.3%

Length

2022-12-04T23:33:29.380168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:29.476167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 334
56.5%
1.0 196
33.2%
3.0 60
 
10.2%
0.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 592
33.4%
. 591
33.3%
2 334
18.8%
1 196
 
11.1%
3 60
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1182
66.7%
Other Punctuation 591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 592
50.1%
2 334
28.3%
1 196
 
16.6%
3 60
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 592
33.4%
. 591
33.3%
2 334
18.8%
1 196
 
11.1%
3 60
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 592
33.4%
. 591
33.3%
2 334
18.8%
1 196
 
11.1%
3 60
 
3.4%

rldv5
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct31
Distinct (%)6.5%
Missing425
Missing (%)47.3%
Infinite0
Infinite (%)0.0%
Mean14.398734
Minimum2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:29.570168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q110
median14
Q318
95-th percentile25
Maximum36
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.7029422
Coefficient of variation (CV)0.39607247
Kurtosis0.023896529
Mean14.398734
Median Absolute Deviation (MAD)4
Skewness0.48429621
Sum6825
Variance32.52355
MonotonicityNot monotonic
2022-12-04T23:33:29.671167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
10 42
 
4.7%
11 34
 
3.8%
12 34
 
3.8%
14 33
 
3.7%
13 33
 
3.7%
15 30
 
3.3%
17 27
 
3.0%
20 27
 
3.0%
18 22
 
2.4%
19 20
 
2.2%
Other values (21) 172
19.1%
(Missing) 425
47.3%
ValueCountFrequency (%)
2 1
 
0.1%
3 1
 
0.1%
4 9
 
1.0%
5 9
 
1.0%
6 12
 
1.3%
7 16
 
1.8%
8 19
2.1%
9 20
2.2%
10 42
4.7%
11 34
3.8%
ValueCountFrequency (%)
36 1
 
0.1%
31 2
 
0.2%
30 1
 
0.1%
29 1
 
0.1%
28 3
 
0.3%
27 4
 
0.4%
26 4
 
0.4%
25 11
1.2%
24 10
1.1%
23 7
0.8%

rldv5e
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct141
Distinct (%)18.6%
Missing142
Missing (%)15.8%
Infinite0
Infinite (%)0.0%
Mean54.914135
Minimum2
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:29.793169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q112
median19
Q3102
95-th percentile176
Maximum270
Range268
Interquartile range (IQR)90

Descriptive statistics

Standard deviation60.309425
Coefficient of variation (CV)1.0982496
Kurtosis0.27208518
Mean54.914135
Median Absolute Deviation (MAD)10
Skewness1.1855355
Sum41570
Variance3637.2267
MonotonicityNot monotonic
2022-12-04T23:33:29.927167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 42
 
4.7%
11 38
 
4.2%
15 33
 
3.7%
10 32
 
3.6%
13 30
 
3.3%
17 30
 
3.3%
9 27
 
3.0%
18 26
 
2.9%
12 26
 
2.9%
8 23
 
2.6%
Other values (131) 450
50.1%
(Missing) 142
 
15.8%
ValueCountFrequency (%)
2 1
 
0.1%
3 3
 
0.3%
4 7
 
0.8%
5 6
 
0.7%
6 18
2.0%
7 23
2.6%
8 23
2.6%
9 27
3.0%
10 32
3.6%
11 38
4.2%
ValueCountFrequency (%)
270 1
 
0.1%
253 1
 
0.1%
252 1
 
0.1%
240 1
 
0.1%
231 1
 
0.1%
230 2
0.2%
227 1
 
0.1%
225 1
 
0.1%
222 1
 
0.1%
220 3
0.3%

ca
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.7%
Missing608
Missing (%)67.6%
Memory size40.9 KiB
0.0
171 
1.0
63 
2.0
37 
3.0
19 
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters873
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row9.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 171
 
19.0%
1.0 63
 
7.0%
2.0 37
 
4.1%
3.0 19
 
2.1%
9.0 1
 
0.1%
(Missing) 608
67.6%

Length

2022-12-04T23:33:30.039167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:30.137168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 171
58.8%
1.0 63
 
21.6%
2.0 37
 
12.7%
3.0 19
 
6.5%
9.0 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 462
52.9%
. 291
33.3%
1 63
 
7.2%
2 37
 
4.2%
3 19
 
2.2%
9 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 582
66.7%
Other Punctuation 291
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 462
79.4%
1 63
 
10.8%
2 37
 
6.4%
3 19
 
3.3%
9 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 873
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 462
52.9%
. 291
33.3%
1 63
 
7.2%
2 37
 
4.2%
3 19
 
2.2%
9 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 462
52.9%
. 291
33.3%
1 63
 
7.2%
2 37
 
4.2%
3 19
 
2.2%
9 1
 
0.1%

restef
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct24
Distinct (%)85.7%
Missing871
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean0.53107143
Minimum0.22
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:30.230168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.3105
Q10.4075
median0.57
Q30.625
95-th percentile0.746
Maximum0.8
Range0.58
Interquartile range (IQR)0.2175

Descriptive statistics

Standard deviation0.14619468
Coefficient of variation (CV)0.27528251
Kurtosis-0.61738075
Mean0.53107143
Median Absolute Deviation (MAD)0.11
Skewness-0.2094494
Sum14.87
Variance0.021372884
MonotonicityNot monotonic
2022-12-04T23:33:30.331169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.59 2
 
0.2%
0.45 2
 
0.2%
0.64 2
 
0.2%
0.6 2
 
0.2%
0.5 1
 
0.1%
0.22 1
 
0.1%
0.41 1
 
0.1%
0.7 1
 
0.1%
0.58 1
 
0.1%
0.76 1
 
0.1%
Other values (14) 14
 
1.6%
(Missing) 871
96.9%
ValueCountFrequency (%)
0.22 1
0.1%
0.3 1
0.1%
0.33 1
0.1%
0.36 1
0.1%
0.38 1
0.1%
0.39 1
0.1%
0.4 1
0.1%
0.41 1
0.1%
0.45 2
0.2%
0.48 1
0.1%
ValueCountFrequency (%)
0.8 1
0.1%
0.76 1
0.1%
0.72 1
0.1%
0.7 1
0.1%
0.67 1
0.1%
0.64 2
0.2%
0.62 1
0.1%
0.61 1
0.1%
0.6 2
0.2%
0.59 2
0.2%

restwm
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)13.3%
Missing869
Missing (%)96.7%
Memory size35.8 KiB
0.0
13 
2.0
1.0
3.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 13
 
1.4%
2.0 8
 
0.9%
1.0 6
 
0.7%
3.0 3
 
0.3%
(Missing) 869
96.7%

Length

2022-12-04T23:33:30.436168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:30.533170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 13
43.3%
2.0 8
26.7%
1.0 6
20.0%
3.0 3
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 43
47.8%
. 30
33.3%
2 8
 
8.9%
1 6
 
6.7%
3 3
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Other Punctuation 30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43
71.7%
2 8
 
13.3%
1 6
 
10.0%
3 3
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43
47.8%
. 30
33.3%
2 8
 
8.9%
1 6
 
6.7%
3 3
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43
47.8%
. 30
33.3%
2 8
 
8.9%
1 6
 
6.7%
3 3
 
3.3%

exeref
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing897
Missing (%)99.8%
Memory size35.3 KiB
0.6
0.5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.6
2nd row0.5

Common Values

ValueCountFrequency (%)
0.6 1
 
0.1%
0.5 1
 
0.1%
(Missing) 897
99.8%

Length

2022-12-04T23:33:30.627168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:30.724168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.6 1
50.0%
0.5 1
50.0%

Most occurring characters

ValueCountFrequency (%)
0 2
33.3%
. 2
33.3%
6 1
16.7%
5 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
66.7%
Other Punctuation 2
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2
50.0%
6 1
25.0%
5 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2
33.3%
. 2
33.3%
6 1
16.7%
5 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
33.3%
. 2
33.3%
6 1
16.7%
5 1
16.7%

exerwm
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)40.0%
Missing894
Missing (%)99.4%
Memory size35.3 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4
 
0.4%
1.0 1
 
0.1%
(Missing) 894
99.4%

Length

2022-12-04T23:33:30.806169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:30.894169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4
80.0%
1.0 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 9
60.0%
. 5
33.3%
1 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9
90.0%
1 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9
60.0%
. 5
33.3%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9
60.0%
. 5
33.3%
1 1
 
6.7%

thal
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)1.7%
Missing477
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean5.0189573
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:30.963167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9493883
Coefficient of variation (CV)0.38840504
Kurtosis-1.8352188
Mean5.0189573
Median Absolute Deviation (MAD)1
Skewness-0.12532101
Sum2118
Variance3.8001148
MonotonicityNot monotonic
2022-12-04T23:33:31.040170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 189
 
21.0%
7 182
 
20.2%
6 42
 
4.7%
1 4
 
0.4%
5 3
 
0.3%
4 1
 
0.1%
2 1
 
0.1%
(Missing) 477
53.1%
ValueCountFrequency (%)
1 4
 
0.4%
2 1
 
0.1%
3 189
21.0%
4 1
 
0.1%
5 3
 
0.3%
6 42
 
4.7%
7 182
20.2%
ValueCountFrequency (%)
7 182
20.2%
6 42
 
4.7%
5 3
 
0.3%
4 1
 
0.1%
3 189
21.0%
2 1
 
0.1%
1 4
 
0.4%

num
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
404 
1
191 
3
132 
2
130 
4
42 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row3
5th row0

Common Values

ValueCountFrequency (%)
0 404
44.9%
1 191
21.2%
3 132
 
14.7%
2 130
 
14.5%
4 42
 
4.7%

Length

2022-12-04T23:33:31.131169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:31.232167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 404
44.9%
1 191
21.2%
3 132
 
14.7%
2 130
 
14.5%
4 42
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 404
44.9%
1 191
21.2%
3 132
 
14.7%
2 130
 
14.5%
4 42
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 404
44.9%
1 191
21.2%
3 132
 
14.7%
2 130
 
14.5%
4 42
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 404
44.9%
1 191
21.2%
3 132
 
14.7%
2 130
 
14.5%
4 42
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 404
44.9%
1 191
21.2%
3 132
 
14.7%
2 130
 
14.5%
4 42
 
4.7%

cathef
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct88
Distinct (%)28.3%
Missing588
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean27.623119
Minimum0.22
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-12-04T23:33:31.350168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.475
Q10.685
median0.82
Q363
95-th percentile73
Maximum86
Range85.78
Interquartile range (IQR)62.315

Descriptive statistics

Standard deviation31.675295
Coefficient of variation (CV)1.1466951
Kurtosis-1.742549
Mean27.623119
Median Absolute Deviation (MAD)0.32
Skewness0.38871942
Sum8590.79
Variance1003.3243
MonotonicityNot monotonic
2022-12-04T23:33:31.476168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 10
 
1.1%
70 9
 
1.0%
0.8 9
 
1.0%
0.73 9
 
1.0%
0.74 8
 
0.9%
68 8
 
0.9%
0.71 8
 
0.9%
65 8
 
0.9%
0.7 7
 
0.8%
63 7
 
0.8%
Other values (78) 228
 
25.4%
(Missing) 588
65.4%
ValueCountFrequency (%)
0.22 2
 
0.2%
0.32 1
 
0.1%
0.4 5
0.6%
0.41 2
 
0.2%
0.42 1
 
0.1%
0.43 2
 
0.2%
0.44 1
 
0.1%
0.45 1
 
0.1%
0.47 1
 
0.1%
0.48 3
0.3%
ValueCountFrequency (%)
86 1
 
0.1%
83 1
 
0.1%
80 1
 
0.1%
79 1
 
0.1%
77 2
0.2%
76 4
0.4%
75 4
0.4%
74 1
 
0.1%
73 3
0.3%
72 3
0.3%

dataset
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.1 KiB
hungarian
294 
cleveland
282 
long-beach-va
200 
switzerland
123 

Length

Max length13
Median length9
Mean length10.163515
Min length9

Characters and Unicode

Total characters9137
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhungarian
2nd rowhungarian
3rd rowhungarian
4th rowhungarian
5th rowhungarian

Common Values

ValueCountFrequency (%)
hungarian 294
32.7%
cleveland 282
31.4%
long-beach-va 200
22.2%
switzerland 123
13.7%

Length

2022-12-04T23:33:31.597167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-04T23:33:31.714168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
hungarian 294
32.7%
cleveland 282
31.4%
long-beach-va 200
22.2%
switzerland 123
13.7%

Most occurring characters

ValueCountFrequency (%)
a 1393
15.2%
n 1193
13.1%
e 887
9.7%
l 887
9.7%
h 494
 
5.4%
g 494
 
5.4%
c 482
 
5.3%
v 482
 
5.3%
r 417
 
4.6%
i 417
 
4.6%
Other values (9) 1991
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8737
95.6%
Dash Punctuation 400
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1393
15.9%
n 1193
13.7%
e 887
10.2%
l 887
10.2%
h 494
 
5.7%
g 494
 
5.7%
c 482
 
5.5%
v 482
 
5.5%
r 417
 
4.8%
i 417
 
4.8%
Other values (8) 1591
18.2%
Dash Punctuation
ValueCountFrequency (%)
- 400
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8737
95.6%
Common 400
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1393
15.9%
n 1193
13.7%
e 887
10.2%
l 887
10.2%
h 494
 
5.7%
g 494
 
5.7%
c 482
 
5.5%
v 482
 
5.5%
r 417
 
4.8%
i 417
 
4.8%
Other values (8) 1591
18.2%
Common
ValueCountFrequency (%)
- 400
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1393
15.2%
n 1193
13.1%
e 887
9.7%
l 887
9.7%
h 494
 
5.4%
g 494
 
5.4%
c 482
 
5.3%
v 482
 
5.3%
r 417
 
4.6%
i 417
 
4.6%
Other values (9) 1991
21.8%

Interactions

2022-12-04T23:33:17.268167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-04T23:32:57.290595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-04T23:33:01.822594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-04T23:33:01.926594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-04T23:33:07.005589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-04T23:33:15.137168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-04T23:33:17.185167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-04T23:33:31.873168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-04T23:33:32.244169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-04T23:33:32.615168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-04T23:33:32.954168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-04T23:33:33.233168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-04T23:33:19.237169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-04T23:33:19.968167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-04T23:33:21.183167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agesexpainlocpainexerrelrestcptrestbpshtncholsmokecigsyearsfbsdmfamhistrestecgdigpropnitrprodiureticprotothaldurthaltimemetthalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeaksloperldv5rldv5ecarestefrestwmexerefexerwmthalnumcathefdataset
04011.00.00.02140.00.0289.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0150.018.0NaN7.0172.086.0200.0110.086.00.00.00.0NaN26.020.0NaNNaNNaNNaNNaNNaN0NaNhungarian
14901.00.00.03160.01.0180.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0NaN10.09.07.0156.0100.0220.0106.090.00.00.01.02.014.013.0NaNNaNNaNNaNNaNNaN1NaNhungarian
23711.00.00.02130.00.0283.0NaNNaNNaN0.0NaNNaN1.00.00.00.00.00.0100.010.0NaN5.098.058.0180.0100.080.00.00.00.0NaN17.014.0NaNNaNNaNNaNNaNNaN0NaNhungarian
34801.01.01.04138.00.0214.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.050.05.04.04.0108.054.0210.0106.086.01.00.01.52.019.022.0NaNNaNNaNNaNNaNNaN3NaNhungarian
45411.00.01.03150.00.0NaNNaNNaNNaN0.0NaNNaN0.00.00.01.01.00.025.02.0NaN3.0122.074.0130.0100.090.00.01.00.0NaN13.09.0NaNNaNNaNNaNNaNNaN0NaNhungarian
53911.00.01.03120.00.0339.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0175.019.0NaN8.0170.086.0198.0100.080.00.00.00.0NaN20.021.0NaNNaNNaNNaNNaNNaN0NaNhungarian
64500.01.00.02130.00.0237.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0100.010.0NaN10.0170.090.0200.0106.084.00.00.00.0NaN11.011.0NaNNaNNaNNaNNaNNaN0NaNhungarian
75411.00.00.02110.00.0208.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0175.019.0NaN7.0142.056.0220.070.070.00.00.00.0NaN11.011.0NaNNaNNaNNaNNaNNaN0NaNhungarian
83711.01.01.04140.01.0207.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.0125.015.013.57.0130.063.0190.0100.080.01.00.01.52.018.019.0NaNNaNNaNNaNNaNNaN1NaNhungarian
94801.00.00.02120.00.0284.0NaNNaNNaN0.0NaNNaN0.00.00.00.00.00.075.07.0NaN4.0120.072.0140.080.080.00.00.00.0NaN6.06.0NaNNaNNaNNaNNaNNaN0NaNhungarian
agesexpainlocpainexerrelrestcptrestbpshtncholsmokecigsyearsfbsdmfamhistrestecgdigpropnitrprodiureticprotothaldurthaltimemetthalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeaksloperldv5rldv5ecarestefrestwmexerefexerwmthalnumcathefdataset
8895101.01.01.04114.01.0258.00.00.00.01.01.01.02.00.01.00.00.00.01.04.0NaN5.096.052.0140.096.074.00.00.01.01.019.020.0NaNNaNNaNNaNNaNNaN00.74long-beach-va
8906211.01.01.04160.01.0254.01.040.047.01.0NaN1.01.01.00.01.01.01.05.03.5NaN2.5108.069.0160.090.080.01.00.03.02.020.019.0NaNNaNNaNNaNNaNNaN40.54long-beach-va
8915311.01.01.04144.01.0300.00.020.010.01.0NaN1.01.00.00.01.00.00.01.04.03.05.0128.076.0150.0102.094.01.00.01.52.012.013.0NaNNaNNaNNaNNaNNaN30.76long-beach-va
8926211.01.01.04158.01.0170.01.020.020.00.0NaN1.01.00.022.01.00.01.05.08.0NaN8.0138.086.0202.098.090.01.00.00.0NaN20.022.0NaNNaNNaNNaNNaNNaN1NaNlong-beach-va
8934611.01.01.04134.01.0310.01.020.021.00.0NaN1.00.00.00.00.00.00.01.05.5NaN7.0126.088.0174.0114.090.00.00.00.0NaN9.07.0NaNNaNNaNNaNNaN3.020.87long-beach-va
8945401.01.01.04127.00.0333.00.00.00.01.0NaN1.01.00.01.01.00.00.01.07.5NaN8.0154.083.0158.084.078.00.00.00.0NaN20.020.0NaNNaNNaNNaNNaNNaN10.76long-beach-va
8956210.00.00.01NaN0.0139.01.015.030.00.0NaN0.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.411.0NaNNaNNaN00.62long-beach-va
8965511.01.01.04122.01.0223.01.020.040.01.0NaN0.01.00.01.01.00.01.05.05.3NaN5.0100.074.0210.0100.070.00.00.00.0NaN6.04.0NaN0.393.0NaNNaN6.020.69long-beach-va
8975811.01.01.04NaN0.0385.00.010.020.01.01.01.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN00.81long-beach-va
8986211.00.00.02120.01.0254.00.00.00.00.0NaN0.02.00.01.00.00.00.01.06.7NaN7.093.067.0164.0110.080.01.00.00.0NaN21.017.0NaNNaNNaNNaNNaNNaN1NaNlong-beach-va